Classification Methods for Recognition of Power Quality Disturbances in Distribution Networks Using Artificial Neural Network

This paper provides the power quality disturbances (PQDs) classification in distribution networks using an artificial neural network (ANN). Various forms of PQDs are considered, including sag, swell, harmonics, flickers, transients, sag with harmonics, and swell with harmonics. The simulation is divided into two parts. The first part is PQDs signals creation and feature extraction using discrete wavelet transform (DWT) and classification by using the dataset from the feature extraction step to be the input of artificial neural network (ANN). The second part is the model testing with the IEEE 9 bus test system with 5 PQDs, which is the most misclassified. The results of the proposed method will be demonstrated using simulation results in MATLAB/Simulink. The results show that the model can classify PQD signals into ten classes. The accuracy of the model is at 99.4%. After testing with the IEEE 9 bus test system, the total accuracy is at 95.6%.